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Fuzzy Logic - artificial intelligence:
In the above logic, we have been concerned with truth whether propositions and sentences are true. But, with some natural language statements, it is difficult to assign a "true" or "false" value. For instance, is the sentence: "Prince Charles is tall" true or false? Some people may say true, and others may say false, so there is an underlying probability that we might also want to represent. This can be attaining with so-called "fuzzy" logics. As such, the originator of fuzzy logics, Lotfi Zadeh, advocates not thinking about specific fuzzy logics but rather thinking about "fuzzification" of current theories, and this is starting to play a part in Artificial Intelligence. The arrangement of logics with programming agents and theories of probability to reason in the light of uncertain knowledge are important areas of Artificial Intelligence research. Various representation schemes such as Stochastic Logic Programs have an aspect of both logic and probability.
Determine the simulation factor- Weather Illustration of simulation is predicting weather (i.e. a weather forecaster). In this case we will consider what/how data is collected,
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#what is decoders? explain with diagram
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Find the generating function to denote the number of ways the sum 9 can be acquired while 2 distinguishable fair dice are tossed and the first shows an even number and the second s
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Proof by Contradiction: Now for forward chaining and backward chaining both have drawbacks. But another approach is to think about proving theorems by contradiction. So we can
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